Introduction

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Very useful

You can access the course materials quickly from

https://laurencefrank.github.io/R

Overview of this course

Program

Goal of this course

1. Learn to programme in R

and build the foundation for a successful scripting career.

2. Learn statistical programming

What is statistical programming?

Broadly speaking, computer programming differs from statistical programming in the following way:

• Computer programming is more focused on software development.

• Statistical programming is more focused on data analysis and the communication of the results.

Source figure: R for Data Science

What is R?

Software

The origin of R

• R is a language and environment for statistical computing and for graphics

• GNU project (100% free software)

• Managed by the R Foundation for Statistical Computing, Vienna, Austria.

• Community-driven

• Based on the object-oriented language S (1975)

Integrated Development Environment

Source figure: R for Data Science

RStudio

• Aggregates all convenient information and procedures into one single place
• Allows you to work in projects
• Manages your code with highlighting
• Gives extra functionality (Shiny, knitr, markdown, LaTeX)
• Allows for integration with version control routines, such as Git.

How does R work

Objects and elements

• R works with objects that consist of elements. The smallest elements are numbers and characters.

  • These elements are assigned to objects.
  • A set of objects can be used to perform calculations
  • Calculations can be presented as functions
  • Functions are used to perform calculations and return new objects, containing calculated (or estimated) elements.

The help

• Everything that is published on the Comprehensive R Archive Network (CRAN) and is aimed at R users, must be accompanied by a help file.

• If you know the name of the function that performs an operation, e.g. anova(), then you just type ?anova or help(anova) in the console.

• If you do not know the name of the function: type ?? followed by your search criterion. For example ??anova returns a list of all help pages that contain the word ‘anova’

• Alternatively, the internet will tell you almost everything you’d like to know and sites such as http://www.stackoverflow.com and http://www.stackexchange.com, as well as Google can be of tremendous help.

  • If you google R related issues; use ‘R:’ as a prefix in your search term

Assigning elements to objects

• Assigning things in R is very straightforward:

  • you just use <-

• For example, if you assign the value 100 (an element) to object a, you would type

a <- 100

When reading that code say: “a gets value 100” in your head.

Good coding practice:

• <- is a pain to type, but don’t use = instead, it will work, but it will cause confusion later.

• Use RStudio’s keyboard shortcut: Alt (or Option) + - (the minus sign).

• Notice that RStudio automatically surrounds <- with spaces (good code formatting practice).

Calling objects

• Calling things in R is also very straightforward:

  • you just use type the name you have given to the object

• For example, we assigned the value 100 to object a. To call object a, we would type

a

## [1] 100

Writing code

This is why we use R-Studio.

Organise your work in RStudio

Use RStudio Projects

Every time you start a new data analysis project, create a new RStudio Project.

Because you want your project to work:

• not only now, but also in a few years;
• when the folder and file paths have changed;
• when collaborators want to run the code on their computer.

RStudio Projects create a convention that guarantees that the project can be moved around on your computer or onto other computers and will still “just work”:

• all code and outputs are stored in one set location;
• relative file paths are created;
• a clean R environment is created every time you open it.

Example structure for data analysis project

Practical A